With the meteoric growth of the Internet in recent years, there has been an associated rise in many if not all current aspects relating to the Internet, as well as development of many new uses for this ubiquitous communication tool. One such area that has witnessed substantial growth over the years is the field of online shopping. Businesses as well individuals are constantly creating new ways to buy and sell products and/or services over the Internet as well as new ways to implement those ideas.
However, in the field of online comparative shopping no conventional system or tool has yet provided an online shopper with adequate functionality, convenience, ease, or trust. Accordingly, one of the most popular means of comparative shopping online is to enter an item (e.g., product or service) into a search engine then manually wade through the results. One difficulty with employing a search engine is that a vast majority of the results are often not related to making a purchase. For example, if a user intends to buy new eyeglasses, she might enter the keyword eyeglasses into a search engine only to find that a majority of the results relate to studies regarding different brands, instructions for determining whether one needs glasses, history of and/or evolution of eyeglasses, manufacture of eyeglasses, different designs, and the like.
Accordingly, it is likely from the outset that many of the results returned by a search engine will be irrelevant to an online shopper. Some search engines today, however, automatically provide advertisements and/or recommendations that are especially directed to online shopping, so the first few results may be expressly intended to be suitable to online shoppers. In addition, a user might supplement the keyword by adding the term(s) “buy”, “purchase” or the like with the keyword eyeglasses to be more certain that the results returned will be relevant. Yet, even assuming the most optimal case that all results are for online shopping, the shopper has no way available to locate the bargains or the best deals short of clicking on each of the results in succession, which can be literally hundreds if not thousands.
Moreover, even with this approach, the shopper is often forced to navigate through a series of links just to get to a purchase page (e.g., a page that provides a purchase indicator such as “add to cart”, etc.) Thus, many extra clicks, time and effort is essentially wasted. Moreover, the shopper will often be forced to repeat the above steps for each different website she visits, usually involving frequent switching between web browser instances further increasing the delays and/or frustration. Further, no matter how competitive a price may appear on any of the given websites visited thus far, the shopper has no way of guessing whether she might find an even better deal on the next link of resulting websites.
While some recommendation sites currently exist, many of these are (as with search engines) ad-based. Thus, the recommendations supplied by these sites (or the first several links provided as search results) are not ultimately designed to benefit the shopper, but rather designed to benefit the vendor who paid for the advertisement/recommendation and the ad publisher who received advertising revenue to display the ad/recommendation. In extreme cases ad-based recommendations can actually be detrimental to the consumer because they may obfuscate the true bargains. Moreover, a user must be aware of these recommendation sites in the first place in order to use them, and often they are designed for only a single product or line of products. In addition, most ad-based recommendation systems are constantly irritating the shopper even during those times she is not interested in shopping.